3-D Multistatic Ground Penetrating Radar Imaging for Augmented Reality Visualization

Abstract

Ground penetrating radar (GPR) is a useful instrument for smarter infrastructure applications, in particular, for the localization and mapping of underground infrastructure and other subsurface assets, due to its ability to sense metallic and nonmetallic buried objects. For instance, air-coupled, multistatic GPR could potentially be employed to quickly produce subsurface maps for public and private stakeholders, enabling rational and more efficient planning of underground infrastructure inspection, maintenance, and construction. An application of interest in such context is a faster identification of underground utilities location and depth by innovative data visualization methods, such as augmented reality. A 3-D model of the subsurface asset is desirable for such applications. However, raw GPR data is often hard to interpret. Imaging algorithms are applied to improve GPR data readability and signal-to-noise ratio by focusing the spread energy. Here, a processing pipeline that takes raw 3-D multistatic GPR data as input and yields a 3-D model as output is proposed. Initially, a 3-D back-projection algorithm is applied to air-coupled, multistatic GPR data to recover buried target localization. An enhancement filter, tailored for tubular structures, is applied to reduce background noise and highlight structures of interest in the 3-D image. This process is successfully applied to three laboratory scenarios of plastic buried targets with different sizes and shapes.